Chronic kidney disease as a predictive factor for poor prognosis in traumatic brain injury among older adults: a case-control study

ObjectiveTraumatic brain injury (TBI) is a highly prevalent neurological disorder that affects a gradually increasing proportion of older adults. Chronic kidney disease (CKD) significantly contributes to global years of life lost, with an estimated one-tenth of the global population affected by CKD. However, it remains unclear whether CKD impacts TBI prognosis. We conducted a case-control study to investigate the clinical outcomes of TBI patients with or without CKD comorbidity and identified the risk factors associated with a poor prognosis.MethodsFrom January 2017 through April 2023, 11 patients with TBI and CKD were included, and 27 control TBI cases with normal kidney function were matched by age, gender, and admission Glasgow Coma Scale (GCS) score as the control group.ResultsThe CKD TBI group had a significantly lower GCS score upon discharge (7.1 ± 5.9) compared to the non-CKD TBI group (13.1 ± 2.6) (p < 0.01). ICU stay time and hospitalization expenses were higher in the CKD group than the non-CKD group, though there were no statistical differences. Additionally, patients in the CKD TBI group had a higher frequency of hospital-acquired infections (54.4%) compared with those in the non-CKD TBI group (7.4%) (p < 0.01). The two groups exhibited no differences in hemoglobin levels, albumin levels, or coagulation function. Logistic regression analysis showed that advanced age, low admission GCS score, elevated blood urea, and creatinine levels were associated with a poor neurological prognosis.ConclusionTBI patients comorbid with CKD have a poorer prognosis than those with normal kidney function

Effect of the Preheating Strategy on the Combustion Process of the Intake Manifold Burner

The intake air preheating is an effective method to improve the cold start performance of diesel engines. The combustion process and ignition probability were investigated in the present study. The average flame area (AFA) during the steady stage of the combustion process was used to evaluate the effects of various factors on combustion. The increase of voltage was found to enhance the combustion process, while the increased diesel flow rate first promoted the combustion before deteriorating it. The increased intake air flow velocity enhanced the combustion within 2.64 m/s, and excessive air flow velocity hindered the combustion from 2.7 to 3 m/s. The cross-distributed vortex clusters in the combustion chamber, periodic diesel evaporation and vortexes with opposite rotation directions in the vicinity of the intake manifold burner were believed to be the main reasons for flame stripping and swirl motion. The temperature rise in the exhaust pipe was recorded to investigate the thermal distribution. The warm air was concentrated in the upper region because of the buoyancy effect of the flame. With the air flow velocity increasing from 1.4 to 10 m/s, the average temperature rise increased first before decreasing, while the combustion efficiency increased due to the increased air flow volume

Effect of the Air Flow on the Combustion Process and Preheating Effect of the Intake Manifold Burner

Diesel engines show poor performance and high emissions under cold-start conditions. The intake manifold burner is an effective method to increase the intake air temperature and improve engine performance. In this paper, a visualization system was employed to investigate the combustion process of the intake manifold burner. The effects of diesel flow rate and airflow velocity on combustion performance were investigated. The combustion process of the intake manifold burner showed four stages: preparing stage A, rapid development stage B, steady-development stage C, and stable stage D. Flame stripping was found in stages C and D, presenting the instability of the combustion process. With the increase in air flow velocity from 1.4 m/s to 3.0 m/s, the flame stripping was enhanced, leading to the increasing combustion instability and regular flame penetration fluctuations. The average temperature rise and combustion efficiency increased with the increasing diesel flow rate, indicating the combustion enhancement. Comparison of temperature rise and combustion efficiency under 2.0 m/s and 10.0 m/s showed that stronger cross wind enhances the heat convection, improving the temperature uniformity and combustion efficiency

Table5_Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.XLSX

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.</p

Table3_Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.XLSX

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.</p

Table2_Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.XLSX

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.</p

Table6_Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.XLSX

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.</p

Table4_Machine learning identified MDK score has prognostic value for idiopathic pulmonary fibrosis based on integrated bulk and single cell expression data.XLSX

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease that poses a significant challenge to medical professionals due to its increasing incidence and prevalence coupled with the limited understanding of its underlying molecular mechanisms. In this study, we employed a novel approach by integrating five expression datasets from bulk tissue with single-cell datasets; they underwent pseudotime trajectory analysis, switch gene selection, and cell communication analysis. Utilizing the prognostic information derived from the GSE47460 dataset, we identified 22 differentially expressed switch genes that were correlated with clinical indicators as important genes. Among these genes, we found that the midkine (MDK) gene has the potential to serve as a marker of Idiopathic pulmonary fibrosis because its cellular communicating genes are differentially expressed in the epithelial cells. We then utilized midkine and its cellular communication-related genes to calculate the midkine score. Machine learning models were further constructed through midkine and related genes to predict Idiopathic pulmonary fibrosis disease through the bulk gene expression datasets. The midkine score demonstrated a correlation with clinical indexes, and the machine learning model achieved an AUC of 0.94 and 0.86 in the Idiopathic pulmonary fibrosis classification task based on lung tissue samples and peripheral blood mononuclear cell samples, respectively. Our findings offer valuable insights into the pathogenesis of Idiopathic pulmonary fibrosis, providing new therapeutic directions and target genes for further investigation.</p